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Abstract

Introduction

The performance of N-terminal pro-brain natriuretic peptide (NT-proBNP) and C-reactive
protein (CRP) to predict clinical outcomes in ICU patients is unimpressive. We aimed
to assess the prognostic value of NT-proBNP, CRP or the combination of both in unselected
medical ICU patients.

Methods

A total of 576 consecutive patients were screened for eligibility and followed up
during the ICU stay. We collected each patient's baseline characteristics including
the Acute Physiology and Chronic Health Evaluation II (APACHE-II) score, NT-proBNP
and CRP levels. The primary outcome was ICU mortality. Potential predictors were analyzed
for possible association with outcomes. We also evaluated the ability of NT-proBNP
and CRP additive to APACHE-II score to predict ICU mortality by calculation of C-index,
net reclassification improvement (NRI) and integrated discrimination improvement (IDI)
indices.

Conclusions

In unselected medical ICU patients, NT-proBNP and CRP can serve as independent predictors
of ICU mortality and addition of NT-proBNP or CRP or both to APACHE-II score significantly
improves the ability to predict ICU mortality. NT-proBNP appears to be useful for
predicting ICU outcomes in cardiac patients.

Introduction

N-terminal pro-brain natriuretic peptide (NT-proBNP) is the inactive polypeptide of
the pre-prohormone brain natriuretic peptide (BNP). It is synthesized in the cardiac
myocytes in response to hemodynamic stress [1] or inflammatory status [2]. Over the last decade, some studies have indicated that NT-proBNP testing greatly
increased the accuracy of the diagnosis of heart failure in patients with dyspnea
[3,4]. NT-proBNP can also serve as a novel, independent predictor of prognosis in cardiovascular
patients [5-7] as well as in the general population [8]. During the past few years, several studies [9-16] have focused on the potential value of NT-proBNP for prognosis of intensive care
unit (ICU) patients, but the performance of NT-proBNP to predict adverse outcome in
those patients is unimpressive [17]. First, the results of those studies have been conflicting. Several studies have
shown that NT-proBNP could serve as an independent predictor of greater mortality
in patients with cardiogenic shock [9], septic shock [10], severe sepsis [11], as well as in noncardiac [12-14] or unselected ICU patients [15], while another study [16] demonstrated that NT-proBNP failed to predict short-term mortality of ICU patients
with hypoxic respiratory failure. Second, most of these studies were rather small
and confounded by some factors, such as cardiovascular disease, renal insufficiency,
or inflammation [17], although the prevalence of these conditions among patients admitted to ICU is generally
high.

C-reactive protein (CRP) is an extremely sensitive objective marker of inflammation,
tissue damage, and infection. Its ability to provide predictive value of long-term
outcomes in ICU patients was just investigated in limited studies [18-20]. There were less data about the predictive value of CRP for short-term mortality
[21,22]. In addition, although NT-proBNP and CRP have been shown to be predictors of adverse
outcomes in ICU patients, the predictive value of the combination of both for outcomes
has not been investigated.

Currently, the Acute Physiology and Chronic Health Evaluation II (APACHE-II) score
is one of the most common models used to evaluate ICU patients' condition and predict
their outcomes [23]. The additive ability of NT-proBNP and CRP to APACHE-II score to predict ICU mortality
has rarely been assessed. Traditionally, predictive models have been evaluated by
C-statistic, but this method has been criticized as being insensitive in comparing
models [24] and for having little direct clinical relevance [25]. Several new methods have recently been proposed to evaluate and compare predictive
risk models [26]. Calculation of net reclassification improvement (NRI) and integrated discrimination
improvement (IDI) indices is now frequently being used [27]. We hypothesized that the higher plasma level of NT-proBNP and CRP would be independently
associated with worse clinical outcomes in unselected ICU patients. We, therefore,
undertook a prospective, observational study to assess the prognostic value of NT-proBNP,
CRP or combination of both in a large population of unselected medical ICU patients.
We also evaluated the ability of NT-proBNP and CRP additive to APACHE-II score to
predict ICU mortality by calculation of C-index, NRI and IDI indices.

Materials and methods

Participants

The prospective, observational trial was undertaken between January 2009 and March
2010 at Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine.
Medical patients were eligible for enrollment if they needed to be transferred to
ICU from emergency department or other departments of our hospital (trauma and surgical
patients were not included). The decision to transfer the patients into or out of
ICU was made by at least one critical care expert and one medical expert. Exclusion
criteria were age < 18 years and known pregnancy. Patients who died within four hours
of admission or were discharged from the ICU within four hours of admission were also
excluded because data collection for those patients was difficult. Patients were classified
as cardiac or noncardiac subgroups according to their primary diagnosis. Noncardiac
was defined as a patient with a primary noncardiac diagnosis. Noncardiac did not preclude
a secondary cardiac disease, nor was a preexisting cardiac disease a priori excluded. The study was approved by Shanghai Jiaotong University Xinhua Hospital Ethics
Committee (XHEC2011-002) and in accordance with the Declaration of Helsinki. Because
this was an observational study and all laboratory indices (including CRP and NT-pro-BNP)
observed were commonly measured for all patients in our ICU department, the need for
written informed consent was waived by the review ethical review board.

Study outcomes

At baseline, demographic and clinical characteristics, including the APACHE-II score
(which can range from 0 to 71, with higher scores indicating more severe illness),
were collected. Then the patients were followed up during the ICU stay. The primary
outcome of this analysis was death in the ICU from any cause.

Statistical analysis

Continuous variables and categorical variables were presented as mean value ± SD and
%, respectively. But CRP, NT-proBNP and eGFR values were reported as median (95% confidence
interval) and then logarithmically normalized (presented as log-CRP, log-NT-proBNP
and log-eGFR, respectively) for statistical calculations because they were skewed.
Baseline characteristics between survivals and non-survivals were compared with unpaired
Student's t-test or Mann-Whitney test for continuous variables and chi-square or Fisher's exact
tests for categorical variables. Univariate logistic regression analyses were performed
to examine the association between mortality and each of the predictors separately.
We also conducted a forward stepwise multivariate logistic regression to determine
the independent predictors of ICU mortality. A criterion of P < 0.05 for entry and a P ≥ 0.10 for removal was imposed in this procedure. Cox & Snell R Square and Nagelkerke
R Square were calculated for assessing the goodness of fit of the models [30]. Odds ratios (ORs) for continuous variables were described using standardized ORs,
which were associated with a one standard deviation change in the variable. The receiver
operating characteristic (ROC) curve was used to examine the performance of variables
to predict ICU mortality. The curve represented a plot of sensitivity vs 1-specificity.
The area under the curve (AUC, that is, C-index) was calculated from the ROC curve.
A statistically derived value, based on the Youden index, maximizing the sum of the
sensitivity and specificity was used to define the optimal cut-off value [31]. ROC curve was also constructed for the combination of two or three variables for
predicting ICU mortality according to the Mackinnon and Mulligan's weighted sum rule
[32]. The differences between AUC (C-index) were tested by Hanley-McNeil methods in order
to examine whether the addition of one or both of the biomarkers improved the discrimination
of the model [33]. The increased discriminative value of the biomarkers was further examined by calculation
of NRI and IDI indices described by Pencina et al. [27]. NRI is the net increase versus decrease in risk categories among case patients minus
that among control participants. It requires that there exist a priori meaningful risk categories (we used < 10%, 10% to 30%, and 30% to 50%, and > 50% for
the risk of ICU death) [26]. IDI is the difference in Yates slopes between models, in which the Yates slope is
the mean difference in predicted probabilities between case patients and control participants
[26]. A two-sided P-value of less than 0.05 was considered to indicate statistical significance. All analyses
were performed with SPSS 13.0 software (SPSS Inc., Chicago, Illinois, USA).

Results

Baseline characteristics

In all, 576 consecutive patients (55.7% male; mean age 71.16 ± 16.5 years) were screened
for eligibility. Baseline clinical and laboratory characteristics of the patients
were shown in Table 1. For the full population, the median level of NT-proBNP, CRP and eGFR on admission
was 2,922 (103 to 35,000.00) pg/ml, 39.8 (7.9 to 158.5) mg/L and 58.0 (6.5 to 150.0)
mL/minute/1.73m2, respectively. The mean APACHE-Ⅱ score was 13.6 ± 7.1 points. The primary reasons
for ICU admission were cardiovascular disease and pulmonary disease. A total of 41.9%
of the patients had accompanying infections. A total of 131 (22.7%) patients died
during the ICU hospitalization. Non-survivors were older and in a more severe condition
as reflected by the higher APACHE-II score, were more frequently septic or infectious,
had higher NT-proBNP, CRP, fasting plasma glucose, white blood cell and heart rate,
and had lower eGFR and blood pressure on admission in the ICU as compared with survivors
(Table 1).

Predictors of ICU mortality

Univariate logistic regression analysis demonstrated that those older, with higher
level of NT-proBNP, CRP and fasting plasma glucose, higher APACHE-II score and lower
eGFR had significantly greater hazard of death (Table 2) (Because blood pressure, heart rate, white blood cell counts and hemoglobin levels
had been included in APACHE-II score, they did not enter into the analysis). When
all the observed baseline variables (Table 2) were included in a stepwise multiple logistic model in which ICU mortality was the
dependent variable; only CRP, log-NT-proBNP, APACHE-II score and fasting plasma glucose
could independently predict primary outcome (P = 0.032, 0.011, 0.000 and 0.039, receptively).

Value for CRP and NT-proBNP in prediction of ICU mortality

To evaluate the value for the above independent variables to predict ICU mortality,
ROC curves were drawn (Figure 1). The AUC was calculated as 0.82 ± 0.02 (P < 0.01) for APACHE II score, 0.71 ± 0.03 (P < 0.01) for NT-proBNP and 0.65 ± 0.03 (P < 0.01) for CRP. The AUC of NT-proBNP or CRP was lower than that of APACHE II score
(all P < 0.01). The optimal cutoff value of APACHE II score for predicting death was ≥ 15,
which gave sensitivity of 77.3% and specificity of 72.5%, and of NT-proBNP was ≥ 4,750
ng/ml, which provided sensitivity of 69.5% and specificity of 68.8%. The optimal cutoff
value of CRP (≥ 27 mg/L) provided sensitivity of 75.05% and specificity of 49.5%.

Figure 1.ROC curves for APACHE II score, CRP and NT-proBNP in prediction of ICU mortality. The area under the ROC curve (AUC) of NT-proBNP were larger than that of CRP or
NT-proBNP (all P < 0.01).

Combination of CRP or NT-proBNP or both with APACHE II score for predicting ICU mortality

To further clarify whether CRP or NT-proBNP or the combination of both had an additive
power with APACHE-II score for predicting ICU mortality, we combined one or two biomarkers
with the APACHE-II score to construct new ROC curves (Figure 2). As compared with the APACHE-II score (AUC 0.82 ± 0.02), combination of CRP (AUC
0.83 ± 0.02) or NT-proBNP (AUC 0.83 ± 0.02) or both (AUC 0.84 ± 0.02) with the APACHE-II
score did not significantly increase AUC for predicting ICU mortality (P = 0.74, 0.74 and 0.47, respectively). The combination of CRP and NT-proBNP (AUC 0.72
± 0.03) was inferior to APACHE-II score for predicting ICU mortality (P < 0.01). In addition, the forward stepwise logistic regression showed that the addition
of NT-proBNP or both biomarkers to the APACHE-II score slightly increased the ability
of the model to predict ICU mortality. The Cox & Snell R Square and Nagelkerke R Square
in the model were slightly increased. (Table 3) However, when using new statistical analysis methods (NRI and IDI indices) which
are more sensitive than the above statistics, we found that the addition of NT-proBNP
or CRP or both to the APACHE-II score significantly improved the ability to predict
the outcome (Table 4). The addition of NT-proBNP to he APACHE-II score gave an IDI of 6.6% (P = 0.003) and NRI of 16.6% (P = 0.007). The addition of CRP to the APACHE-II score provided an IDI of 5.6% (P = 0.026) and NRI of 12.1% (P = 0.023), and the addition of both markers to the APACHE-II score yielded an IDI of
7.5% (P = 0.002) and NRI of 17.9% (P = 0.002).

Figure 2.ROC curves for combination of two or three variables among CRP, NT-proBNP and APACHE-II
score. Combination of CRP or NT-proBNP or both with APACHE-II score did not significantly
increase AUC with regard to perdition of ICU mortality (all P > 0.05). Combination of CRP and NT-proBNP was inferior to APACHE-II score alone for
predicting ICU mortality (P < 0.01).

Discussion

In this large scale study of 576 unselected medical ICU patients, we found that NT-proBNP
and CRP independently predicted ICU mortality even after adjustment for the APACHE
II score and multiple potential confounders including eGFR, age, and so on. Although
the predictive ability was lower as compared with the APACHE II score, the addition
of CRP or NT-proBNP or both to the APACHE II score could significantly improve the
ability to predict ICU mortality, as demonstrated by IDI and NRI indices. NT-proBNP
appeared to be more useful for predicting ICU outcomes in cardiac patients. To our
knowledge, this is the first large-scale study to evaluate the ability of NT-proBNP
and CRP added to the APACHE-II score to predict ICU mortality, especially using the
new statistics method, that is, the NRI and IDI indices.

BNP and NT-proBNP have become promising biomarkers recently. They have been used as
tools for risk stratification in cardiac patients [3-7], the general population [8] and ICU patients [9-18]. Most of the studies investigating the predictive value of NT-proBNP in ICU patients
were confounded by some factors, such as renal insufficiency or inflammation. Our
study showed that NT-proBNP independently predicted ICU mortality in unselected patients
even after adjustment for the APACHE II score and other potential confounders, including
age, renal insufficiency (eGFR), and inflammation (CRP). However, the ability of NT-proBNP
to predict ICU mortality was lower than that of the APACHE II score (AUC: 0.82 ± 0.02
vs 0.71 ± 0.03, P < 0.01; OR: 1.454 vs 3.532). The C statistic is the most commonly used method of determining
model discrimination. In this method, we found that the addition of one or both the
biomarkers to the APACHE II score did not significantly improve the predictive ability
(AUC). However, the sole reliance on the C-statistic for the evaluation of predictors
has been questioned, because very large independent associations of a new marker with
the outcome are required to result in a significant increase in the C statistic [24,25]. In the present study, we also used a more sensitive test of improvement in model
discrimination [27]. We found that the addition of NT-proBNP to the APACHE II score significantly increased
the ability to predict ICU mortality as demonstrated by the IDI (6.6%, P = 0.003) and NRI (16.6%, P = 0.007) indices. NT-proBNP was not an independent predictor of ICU mortality in the
non-cardiac subgroup after adjustment for APACHE II score and CRP. Kotanidou et al. [13] found that NT-proBNP predicted mortality independently after the adjusted APACHE
II score and some inflammatory cytokines levels in non-cardiac ICU patients. But they
used TNF-α, IL-6, and IL-10 rather than CRP and enrolled many surgical and multiple
trauma cases. In the cardiac subgroup, NT-proBNP independently predicted ICU mortality
while the AUC of the APACHE II score was not different from that of NT-proBNP (0.81
± 0.03 vs 0.77 ± 0.04; P > 0.05). The addition of NT-proBNP to the APACHE-II score can obviously increase predictive
ability (IDI = 10.2%, P = 0.018; NRI = 18.5%, P = 0.028). Therefore, although NT-proBNP could predict ICU mortality in unselected
medical patents, it appeared to be more useful in cardiac patients than in non-cardiac
patients.

LV wall tension is regarded as the primary mechanism regulating NT-proBNP secretion
[1]. Other hemodynamic factors that may contribute to NT-proBNP secretion include left
ventricular diastolic dysfunction and right ventricular overload and dysfunction [10,34]. Other mechanisms proposed to account for high NT-proBNP values include renal dysfunction
[35] and inflammatory status [36,37]. Therefore, patients with high NT-proBNP may have cardiac dysfunction, renal dysfunction
or inflammatory status. All of these factors have shown to be a frequent and important
factors in determining the outcome of critically ill patients [18-21,38]. This is the reason why NT-proBNP can be used as a predictor of outcomes in ICU patients.

CRP has long been considered to be a distinct and sensitive biomarker of inflammation,
tissue damage, and infection. Some studies also suggest that CRP may be an indicator
of organ failure [22]. Only a few studies have tested its value for predicting outcome in ICU patients
[18-22]. However, most of these studies observed the post-ICU outcomes but not ICU mortality.
NT-proBNP was not included in their analyses, either. One previous study showed no
predictive value of CRP for in-hospital mortality, even in univariate analysis [21]. The scope of the study was rather small (N = 103) and, thus, the statistical power was less than that of our study. Moreover,
the endpoint of the previous study was in-hospital mortality but not ICU mortality.
The present study revealed that CRP was also an independent predictor of ICU mortality
in unselected patients or non-cardiac patients. Although the C-statistic showed the
addition of CRP to the APACHE-II score in prediction of ICU mortality did not significantly
improve the predictive ability, NRI (12.1%, P = 0.026) and IDI (5.6%, P = 0.023) were statistically significant.

Several limitations of our study should be mentioned. First, neither echocardiography
was performed nor cardiac function assessed in the present study. The division of
subgroups was according to primary admission cause. Thus patients in the non-cardiac
group may also have cardiac disease and cardiac dysfunction. However, patients with
cardiac diseases as the primary principal diagnosis leading to ICU admission must
have cardiac diseases. The statistical conclusion drawn from the cardiac group was
appropriate. Second, this was a single-center study, and participants did not include
surgery and trauma patients. The value for NT-proBNP in prediction of adverse outcome
would be a bit different if the population was different. At last, a limitation of
the net reclassification improvement and other reclassification measures is that they
depend on the particular categories used [26]. We had used < 10%, 10% to 30%, and 30% to 50%, and > 50% for the risk of ICU death
as risk categories. But there are still no well-recognized risk categories now. If
the risk categories used had been different, the NRI would be a bit different.

Conclusions

In this large-scale study of unselected ICU patients, we confirmed that NT-proBNP
and CRP can serve as moderate independent predictors of ICU mortality. Although the
predictive ability was lower compared with the APACHE II score, but the addition of
CRP or NT-proBNP or both to the APACHE II score could significantly improve the ability
to predict ICU mortality, as demonstrated by IDI and NRI indices. NT-proBNP appeared
to be more useful for predicting ICU outcomes in cardiac patients.

Key messages

● NT-proBNP and CRP independently predicted ICU mortality even after adjustment for
the APACHE II score and multiple potential confounders.

● The ability of NT-proBNP and CRP to predict ICU mortality was lower compared with
the APACHE II score.

● The addition of CRP or NT-proBNP or both to the APACHE II score could significantly
improve the ability to predict ICU mortality as demonstrated by IDI and NRI indices.

● NT-proBNP appeared to be more useful for predicting ICU outcomes in cardiac patients.

Competing interests

The authors declare that they have no competing interests.

Authors' contributions

WeP and JG participated in the design of the study and performed the statistical analysis
and drafted the manuscript. FW and SP carried out data collection, contributed to
the design of the study and helped to draft the manuscript. QG and SW participated
in the data collection. All authors read and approved the final manuscript.